With the escalating amount of data available online, recommender systems became very popular, especially on web sites. As known in the art, recommender systems are systems that recommend items to users. Such systems have various applications such as helping users find web pages that interest them, recommending products to customers in e-commerce websites, recommending TV programs to users of interactive TV and displaying personalized advertisements. There are many types of recommender systems ranging from manually predefined un-personalized recommendations to fully automatic general purpose recommendation engines.
Recommender systems are software tools aimed at supporting their users in decision-making. Recommender Systems are supposed to be used by people that do not have sufficient personal experience or competence to evaluate the, potentially overwhelming, number of alternatives offered in a web site. Specifically, in many web-based sites the aim of recommender systems is to suggest items to the users.
One type of recommendation systems is context-aware. US 2009/0193099 (Partige et al.) and US 2009/0281875 (Beatrice) disclose context-aware recommender systems which incorporate user's contextual information such as time, location, into the recommendation. Another type of recommendation systems is location-based. Location-based recommender systems aim to find the most relevant items to the current location of the user. For example, a restaurant recommender system recommends restaurants according to the geographic proximity of the user to the recommended restaurants. US 2009/0281875 discloses another type of recommender systems, namely travel recommender systems. Travel recommender systems recommend users on places to visit. Once the user input his destination the system recommends sites to see.
However, the traditional methods hereinabove match the current location of the user to the location of the items available for recommendation and recommend item in the geographic proximity of the user. In many cases such solution prevents the user from receiving recommendations regarding items from a far surrounding which may be of high interest to the user. Additionally, some traditional methods require to infer the preferences of the user from the user's contextual information which in many cases is not related to the preferences of the user and in some cases may even deceive, for example if a user writes “I would prefer any thing other then Pizza”, a context-aware recommender system may infer that the user prefer Pizza and recommends him pizzerias in his geographic proximity.
GeoTagging is the process of adding geographical identification metadata to various media such as photographs, video, websites, SMS messages, RSS feeds, and other. GeoTagging is a form of geospatial metadata. These data usually consist of latitude and longitude coordinates, though it can also include altitude, bearing, distance, accuracy data, and place names. One of the most common uses of GeoTagging is by photographs which takes geoTagged photographs. GeoTagging can help users to find a wide variety of location-specific information. For instance, one can find images taken near a given location by entering latitude and longitude coordinates into a suitable image search engine. GeoTagging-enabled information services can also be used to find location-based news, websites, or other resources. GeoTagging can tell users the location of the content of a given picture or other media or the point of view, and conversely on some media platforms show media relevant to a given location.
In some cases users are interested in news coming from a specific location disregarding their current or future physical location. For example, a reader might be interested in news from his born place even if he does not live there anymore. Readers might be also interested in reading news from different conflict areas. News recommender systems according the traditional methods are frequently use content based recommendation methods. Due to the fact that the location name is frequently mentioned in the article text, content based methods can take the geo-location into consideration to some extent. However they might neglect obvious geo-relations. For example if a user likes an article which mentions the city “Frankfurt” then the content based recommender systems will probably know to recommend other news coming from “Frankfurt”. However it will not be able to recommend news coming from the city “Darmstadt” even if it is only 20 km away.
Some existing news recommender systems are using ‘nearest neighbors’ algorithms to calculate the content distance between two articles. These systems can be adjusted to take into consideration also the geographical distance between the locations of two articles in addition to the content distance. However, these kinds of solutions are usually user based and are only capable of recommending articles located closely to previously clicked news and cannot be used to recommend entirely new locations. For example, if a reader reads news coming from ancient cities such as Rome, Jerusalem, and Lisbon then he might be interested in news coming from other ancient cities such as Athens and Plovdiv.
Collaborative filtering (CF) is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of the CF approach is that those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes). These predictions are specific to the user, but use information gleaned from many users.
U.S. Pat. No. 7,440,943 discloses a collaborative filtering systems for improving the recommendation results achieved by the recommendation system. However while existing collaborative filtering systems find similarities based on users consumptions or rating of items. We also take into consideration the location attached to the item. Theoretically, collaborative filtering can be used for discovering new interesting locations and based on this information to find related items. However, it is not practical to simply refer to the geo-location as “items” like in any other CF application, because in fine-grained application there will be tremendous number of locations or even a much higher number of geo-location information that might be pointing to the same location.
It would therefore be highly desirable to provide a recommendation system that overcomes the drawbacks of the existing systems. Such a system would recommend objects based on geo-tagged data attached to them, rather than based on the location of the user and the items.
It is therefore an object of the present invention to provide a method for recommending objects from certain locations which are of interest to the user.
It is another object of the present invention to provide a method for recommending fine-grained geo-tagged items.
It is yet another object of the present invention to provide a method which extends existing nearest neighbors collaborative filtering systems for improving the recommendation results.
Other objects and advantages of the invention will become apparent as the description proceeds.